检索结果-内蒙古大学图书馆

An interactive image segmentation method for lithological boundary detection: A rapid mapping tool for geologists

COMPUTERS & GEOSCIENCES 2017年 100卷 27-40页

作者： Vasuki, Yathunanthan Holden, Eun-Jung Kovesi, Peter Micklethwaite, Steven Univ Western Australia Sch Earth & Environm Ctr Explorat Targeting 35 Stirling Highway Crawley WA 6009 Australia Monash Univ Sch Earth Atmosphere & Environm Clayton Vic 3800 Australia

Large volumes of images are collected by geoscientists using remote sensing platforms. Manual analysis of these images is a time consuming task and there is a need for fast and robust image interpretation tools. In particular the reliable mapping of lithological boundaries is a critical step for geological interpretation. In this contribution we developed an interactive image segmentation algorithm that harnesses the geologist's input and exploits automated image analysis to provide a practical tool for Ethology boundary detection, using photographic images of rock surfaces. In the proposed method, the user is expected to draw rough markings to indicate the locations of different geological units in the image. image segmentation is performed by segmenting regions based on their homogeneity in colour. This results in a high density of segmented regions which are then iteratively merged based on the colour of different geological units and the user input. Finally, a post-processing step allows the user to edit the boundaries. An experiment was conducted using photographic rock surface images collected by a UAV and a handheld digital camera. The proposed technique was applied to detect lithology boundaries. It was found that the proposed method reduced the interpretation time by a factor of four relative to manual segmentation, while achieving more than 96% similarity in boundary detection. As a result the proposed method has the potential to provide practical support for interpreting large volume of complex geological images.

关键词： interactive image segmentation Lithological boundary detection Region merging Multi label segmentation Real time boundary editing

来源：评论

学校读者我要写书评

暂无评论

Spiking Neural P System with weight model of majority voting technique for reliable interactive image segmentation

引用

NEURAL COMPUTING & APPLICATIONS 2023年第12期35卷 9035-9051页

作者： Dalvand, Mehran Fathi, Abdolhossein Kamran, Arezoo Razi Univ Dept Comp Engn & Informat Technol Kermanshah Iran

interactive image segmentation is a method for precisely segmenting of the object from background using information entered by the user. However, most interactive segmentation techniques are sensitive to the location and the number of seed points. To obtain a satisfactory result, the user should repeat the segmentation process over and over, and also based on employed technique, it may work well in some limited conditions and applications. To overcome these limitations and enhance the robustness of interactive image segmentation algorithm, this paper proposes a parallel fusion model using the majority voting technique, which not only is more reliable than existing methods, but also requires less user interaction. To this end, at first the input image is segmented by several segmentation methods independently. Then the obtained results are combined using majority voting technique to extract final segmentation result. To reduce the computational overhead of the proposed scheme, a spiking neural-like P system model for parallel implementation of majority voting technique is also proposed. The proposed model has been evaluated and compared with state-of-the-art methods using different metrics, and the obtained results show its efficiency compared to other methods.

关键词： interactive image segmentation Majority voting Spiking neural-like P system Membrane computing

来源：评论

学校读者我要写书评

暂无评论

Diffusion map based interactive image segmentation

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2017年第16期76卷 17497-17509页

作者： Wang, Xun Jin, Jianqiu Yang, Bailin Zhejiang Gongshang Univ Sch Comp Sci & Informat Engn 18 Xuezheng St Hangzhou Zhejiang Peoples R China Zhejiang Gongshang Univ Coll Comp Sci & Informat Engn 18 Xuezheng St Hangzhou Zhejiang Peoples R China Zhejiang Gongshang Univ Dept Comp & Elect Engn 18 Xuezheng St Hangzhou Zhejiang Peoples R China

image segmentation is a necessary but difficult task in many image processing applications. Unlike conventional auto-segmentation, semi-supervised image segmentation involves a moderate amount of user interaction. In this paper, a novel interactive image segmentation method based on diffusion maps is proposed, which can better account for the distribution of pixels in feature space. Our method replaces the pixel features of Euclidean distance with diffusion maps and performs feature optimization and dimensionality reduction in order to achieve a final feature vector, with the assistance of user interaction. On this basis, L0 gradient minimization is applied to achieve steady segmentation results. Finally, the K-means clustering algorithm is applied to perfect the segmentation. Experiments show that our method can obtain better segmentation results than several state-of-the-art interactive image segmentation methods.

关键词： interactive image segmentation Diffusion maps L0 gradient minimization

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised interactive image segmentation

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第8期34卷 6797-6808页

作者： Shi, Qingxuan Li, Yihang Di, Huijun Wu, Enyi Hebei Univ Hebei Machine Vis Engn Res Ctr Baoding 071000 Peoples R China Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Beijing Inst Technol Sch Comp Sci Beijing 100811 Peoples R China

Although interactive image segmentation techniques have made significant progress, supervised learning-based methods rely heavily on large-scale labeled data which is difficult to obtain in certain domains such as medicine, biology, etc. Models trained on natural images also struggle to achieve satisfactory results when directly applied to these domains. To solve this dilemma, we propose a Self-supervised interactive segmentation (SIS) method that achieves superior generalization performance. By clustering features from unlabeled data, we obtain classifiers that assign pseudo-labels to pixels in images. After refinement by super-pixel voting, these pseudo-labels are then used to train our segmentation network. To enable our network to better adapt to cross-domain images, we introduce correction learning and anti-forgetting regularization to conduct test-time adaptation. Our experiment results on five datasets show that our approach significantly outperforms other interactive segmentation methods across natural image datasets in the same conditions and achieves even better performance than some supervised methods when across to medical image domain. The code and models are available at https://***/leal0110/SIS.

关键词： image segmentation Feature extraction Task analysis Medical diagnostic imaging Training Adaptation models Annotations interactive image segmentation self-supervised learning test-time adaptation generalization

来源：评论

学校读者我要写书评

暂无评论

Diffusive likelihood for interactive image segmentation

引用

PATTERN RECOGNITION 2018年 79卷 440-451页

作者： Wang, Tao Ji, Zexuan Sun, Quansen Chen, Qiang Ge, Qi Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 St Xiao Ling Wei Nanjing 210094 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing Jiangsu Peoples R China

The performance of conventional interactive image segmentation methods is strongly affected by seed quantity and position, and it is difficult for them to maintain global data coherence due to the bias that is caused by limited interactions. Furthermore, the pixel-level relationships in these methods are too local to capture long-range connectivity cues, which often causes them to obtain under-segmented results. To solve these problems, this paper proposes an interactive segmentation method that is based on likelihood diffusion and perceptual learning. The diffusive likelihood strategy is proposed for accurately estimating the prior label probability from limited user inputs. Superpixel-level grouping cues are utilized to enforce continuity during the segmentation process. The geometrical adjacency and long-range grouping cues are fused in the proposed framework to ensure that the segmentation results maintain proximity and continuity. The final results can be obtained by applying a joint optimization technique to solve a pair of sub-module functions. Experiments on the Berkeley segmentation data set and the Microsoft GrabCut database demonstrate that the proposed method outperforms state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Likelihood diffusion Perceptual learning Graph cuts

来源：评论

学校读者我要写书评

暂无评论

Click-Pixel Cognition Fusion Network With Balanced Cut for interactive image segmentation

引用

IEEE TRANSACTIONS ON image PROCESSING 2024年 33卷 177-190页

作者： Lin, Jiacheng Xiao, Zhiqiang Wei, Xiaohui Duan, Puhong He, Xuan Dian, Renwei Li, Zhiyong Li, Shutao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha 410081 Peoples R China Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Key Lab Visual Percept & Artificial Intelligence H Changsha 410082 Peoples R China Hunan Univ Sch Robot Changsha 410012 Peoples R China

interactive image segmentation (IIS) has been widely used in various fields, such as medicine, industry, etc. However, some core issues, such as pixel imbalance, remain unresolved so far. Different from existing methods based on pre-processing or post-processing, we analyze the cause of pixel imbalance in depth from the two perspectives of pixel number and pixel difficulty. Based on this, a novel and unified Click-pixel Cognition Fusion network with Balanced Cut (CCF-BC) is proposed in this paper. On the one hand, the Click-pixel Cognition Fusion (CCF) module, inspired by the human cognition mechanism, is designed to increase the number of click-related pixels (namely, positive pixels) being correctly segmented, where the click and visual information are fully fused by using a progressive three-tier interaction strategy. On the other hand, a general loss, Balanced Normalized Focal Loss (BNFL), is proposed. Its core is to use a group of control coefficients related to sample gradients and forces the network to pay more attention to positive and hard-to-segment pixels during training. As a result, BNFL always tends to obtain a balanced cut of positive and negative samples in the decision space. Theoretical analysis shows that the commonly used Focal and BCE losses can be regarded as special cases of BNFL. Experiment results of five well-recognized datasets have shown the superiority of the proposed CCF-BC method compared to other state-of-the-art methods. The source code is publicly available at https://***/lab206/CCF-BC.

关键词： Task analysis Visualization image segmentation Cognition Training Transforms Optimization Click-pixel fusion pixel imbalance problem balanced normalized focal loss interactive image segmentation

来源：评论

学校读者我要写书评

暂无评论

Graph-cut based interactive image segmentation with randomized texton searching

引用

COMPUTER ANIMATION AND VIRTUAL WORLDS 2016年第5期27卷 454-465页

作者： Ma, Wei Zhang, Yu Yang, Luwei Duan, Lijuan Beijing Univ Technol Coll Comp Sci Beijing Peoples R China

In the paper, we present an interactive image-segmentation method in the framework of graph cut, which incorporates not only traditional color and gradient constraints, but also a new type of texture constraint. Given an image with user-input strokes, we first establish the color and texture prior models of the foreground/background. The texture prior model, which is key to establish the texture constraints, is represented by local binary patterns (LBP) histograms. Then, an energy function composed of color, gradient, and texture terms is formulated. At last, by using graph cut, we minimize the energy function to obtain the foreground. In the energy function, the color and gradient terms have similar forms with traditional methods. The texture term in the function is generated using a proposed randomized texton-searching algorithm. First, the algorithm locates an approximately best representative texton for every unknown pixel as foreground and an approximately best one as background, through randomized searching. Second, it computes the LBP histograms of the two textons as the pixel's foreground/background texture descriptors, respectively. Finally, the distances between the descriptors and the foreground/background prior models are used to formulate the texture term. Experimental results demonstrate that our method outperforms traditional ones. Copyright (C) 2015 John Wiley & Sons, Ltd.

关键词： interactive image segmentation graph cut texture constraint LBP randomized texton searching

来源：评论

学校读者我要写书评

暂无评论

Adaptive appearance separation for interactive image segmentation based on Dense CRF

引用

IET image PROCESSING 2019年第1期13卷 142-151页

作者： Peng, Zili Li, Qiaoliang Hunan Normal Univ Key Lab High Performance Comp & Stochast Informat Minist Educ China Coll Math & Comp Sci Changsha 410081 Hunan Peoples R China

interactive segmentation has recently become a hot topic for its wide application. The authors propose an efficacious appearance separation model for interactive binary segmentation, which incorporates the difference of foreground and background colour models and the difference of corresponding geodesic models into the popular densely connected conditional random field (Dense CRF) framework. The proposed method can adaptively set relevant parameter values in this framework according to the characteristics of target images in a per-image manner, therefore, it gets rid of the dependence on specific datasets. After accomplishing a mean-field inference, the authors are able to get satisfactory results without the time-consuming parameter learning process and multiple iterative optimisations. Overall, the proposed approach is highly efficient and mitigates the contradiction between accuracy and segmentation efficiency. In addition, the proposed approach reduces the efforts of scribble-style interaction from users. The experimental results on three famous datasets show that the proposed method is superior to the other five new algorithms released in recent years regarding accuracy, and is faster than or close to them in runtime.

关键词： image colour analysis learning (artificial intelligence) image segmentation adaptive appearance separation interactive image segmentation Dense CRF interactive segmentation hot topic wide application efficacious appearance separation model interactive binary segmentation foreground background colour models corresponding geodesic models popular densely connected conditional random field framework relevant parameter values target images per-image manner mean-field inference time-consuming parameter segmentation efficiency scribble-style interaction

来源：评论

学校读者我要写书评

暂无评论

Adaptive fusion with multi-scale features for interactive image segmentation

引用

APPLIED INTELLIGENCE 2021年第8期51卷 5610-5621页

作者： Ding, Zongyuan Wang, Tao Sun, Quansen Wang, Hongyuan Nanjing Univ Sci & Technol Sch Comp Sci & Engn 200 Xiao Ling Wei St Nanjing 210094 Peoples R China Changzhou Univ Sch Informat Sci & Engn 1 Ge Hu Zhong Lu Changzhou 213164 Jiangsu Peoples R China

Multi-scale features are usually utilized to improve the performance of interactive image segmentation, however, they have varying leverages over the result of segmentation, for example, thinner segmentation results could be achieved by pixel-level features, but sensitive to image noise, and superpixel-level features could provide the semantic perception of the object, but easily lead to over-segmentations. Therefore, we propose an interactive image segmentation algorithm by adaptive fusion with multi-scale features (AFMSF). It intends on combining the multi-scale information adaptively for the segmentation via learning the influence coefficients of multi-scale features. First, multi-scale superpixel layers are generated by controlling the size of superpixels. Based on features of this multi-scale information, the similarity matrices and label priors with pixel-superpixel levels are then obtained. A fusion with diffusion strategy is designed to build the energy function by combining these multi-cues. Finally, the influence coefficient of each scale and the labeling are updated with each other until convergence. The algorithm we proposed is robust to diverse circumstances of objects, the experimental results on public interactive image segmentation datasets Graz, LHI, and MSRC validate the superior performance of the proposed method.

关键词： interactive image segmentation Semi-supervised learning Multi-scale features Fusion with diffusion

来源：评论

学校读者我要写书评

暂无评论

A fully convolutional two-stream fusion network for interactive image segmentation

引用

NEURAL NETWORKS 2019年 109卷 31-42页

作者： Hu, Yang Soltoggio, Andrea Lock, Russell Carter, Steve Loughborough Univ Loughborough Leics England ICE Agcy Poole Dorset England

In this paper, we propose a novel fully convolutional two-stream fusion network (FCTSFN) for interactive image segmentation. The proposed network includes two sub-networks: a two-stream late fusion network (TSLFN) that predicts the foreground at a reduced resolution, and a multi-scale refining network (MSRN) that refines the foreground at full resolution. The TSLFN includes two distinct deep streams followed by a fusion network. The intuition is that, since user interactions are more direct information on foreground/background than the image itself, the two-stream structure of the TSLFN reduces the number of layers between the pure user interaction features and the network output, allowing the user interactions to have a more direct impact on the segmentation result. The MSRN fuses the features from different layers of TSLFN with different scales, in order to seek the local to global information on the foreground to refine the segmentation result at full resolution. We conduct comprehensive experiments on four benchmark datasets. The results show that the proposed network achieves competitive performance compared to current state-of-the-art interactive image segmentation methods.(1) (c) 2018 Elsevier Ltd. All rights reserved.

关键词： interactive image segmentation Fully convolutional network Two-stream network

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：